A method for underwater sonar image matching based on Gaussian distribution clustering

Abstract

An underwater sonar image matching method based on Gaussian distribution clustering, through image registration and optimization of sonar 3D motion parameters, for accurate 3D reconstruction of 2D sonar images, including the following steps: Step A: extract features, establish The matching relationship between features is used to obtain the elevation information of the map; Step B: perform pose estimation, and update the feature map to generate a three-dimensional space map. The invention realizes the feature extraction of the sonar image to the reconstruction of the environmental map covering the elevation information, and provides certain motion attitude estimation information at the same time, which can be used in the field of underwater robot sonar image processing and mapping.

Description

technical field [0001] The invention relates to the fields of ship and marine technology and sonar image processing, in particular to methods for underwater detection and perception and three-dimensional reconstruction of targets, and specifically proposes an underwater sonar image matching method based on Gaussian distribution clustering. Background technique [0002] In the past decade, various feature-based, template-based, region-based, and Fourier-based methods for two-dimensional FS sonar image registration have been explored, and high-resolution two-dimensional (2D) Forward-scanning beam (FS) sonar video systems are commercially available for operation and imaging in turbid waters. But previous studies are generally based on single feature points, and then image registration is formulated as an optimization problem, using a normal distribution transformation. But the choice of grid size involves a trade-off between resolution and computation time and is not necessari...

AI Technical Summary

Problems solved by technology

The distribution of points does not obey the univariate Gaussian distribution. The previous calculation of Gaussian distribution will lead to the wrong representation of the real distribution, and the optimization time of complex grid structure calculation is too long

When the elevation map is generated, the elevation angle will be lost when 3D is projected onto the 2D sonar image, which will cause a large error in the reconstruction target, and the unsatisfactory optimization method will affect image registration and accurate 3D reconstruction

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